Abstract : We propose a cost-based query-adaptive clustering solution for multidimensional objects with spatial extents to speed-up execution of spatial range queries e.g., intersection, containment. Our work was motivated by the emergence of many SDI applications Selective Dissemination of Information bringing out new real challenges for the multidimensional data indexing. Our clustering method aims to meet several application requirements such as scalability many objects with many dimensions and with spatial extents, search performance high rates of spatial range queries, update performance frequent object insertions and deletions, and adaptability to object and query distributions and to system parameters. In this context, the existing indexing solutions e.g., R-trees do not efficiently cope with most of these requirements. Our object clustering drops many properties of classical tree-based indexing structures tree height balance, balanced splits, minimum object bounding in favor of a cost-based clustering strategy. The cost model takes into account the performance characteristics of the execution platform and relies on both data and query distributions to improve the average performance of spatial range queries. Our object clustering is based on grouping spatial objects with similar intervals positions and extents in a reduced subset of dimensions, namely the most selective and discriminatory ones relative to the query distribution. The practical relevance of our clustering approach was demonstrated by a series of experiments involving large collections of multidimensional spatial objects and spatial range queries with uniform and skewed distributions.